<i>SF3B1</i> mutations in primary and secondary myelofibrosis: Clinical, molecular and prognostic correlates
Authors: Giuseppe G. Loscocco, Paola Guglielmelli, Francesco Mannelli, Barbara Mora, Carmela Mannarelli, Giada Rotunno, Fabiana Pancani, Chiara Maccari, Niccolò Bartalucci, Simone Romagnoli, Giacomo Coltro, Francesco Passamonti, Alessandro M. Vannucchi
Journal: American Journal of Hematology (2022)

Myelofibrosis (MF), primary (PMF) or secondary (SMF) to polycythemia vera (PPV-MF) or essential thrombocythemia (PET-MF), is characterized by a complex molecular architecture, including mutations in driver (JAK2, CALR, MPL) and other myeloid neoplasm associated genes.1 Mutations of SF3B1 (splicing factor 3B subunit 1), a component of the U2-small nuclear ribonucleoprotein complex, are uncommon in MF and are reported in <10% of cases.2 Conversely, they are present in >80% of cases of myelodysplastic syndromes (MDS) with ring sideroblasts (MDS-RS) and in MDS/MPN with RS and thrombocytosis (MDS/MPN-RS-T) representing a disease-defining genetic event. Indeed, a proposal to consider SF3B1 mutated MDS as an individual entity characterized by an overall better prognosis, has been made.3 In general, the majority of SF3B1 mutations occur within codon 700 resulting in a large-scale downregulation of messenger RNA (mRNA).4, 5 The characteristics of SF3B1 mutations outside this hotspot, and their downstream effects, are largely unclear; in this regard, SF3B1 K666N in MDS patients was recently associated with distinctive RNA splicing profile, clinical features and worse outcomes.6 While SF3B1 mutations are reportedly lacking prognostic significance in PMF,7, 8 no information are available in the setting of SMF. The main objective of the current study was to compare the prevalence and clinical, molecular and prognostic correlates of SF3B1 mutations in SMF versus PMF patients. After Institutional review board (IRB) approval (#14560), all consecutive patients with PMF and SMF diagnosis, respectively in according to World Health Organization (WHO) and International Working Group for MPN Research and Treatment (IWG-MRT) criteria, and known SF3B1 mutation status, available in our databases, were included in the study. Molecular analysis for driver and non-driver genes was done at diagnosis or first referral, as per previously published methods. Distribution of continuous variables was compared using nonparametric (Mann–Whitney or Kruskal-Wallis) tests, while nominal variables were compared with the Chi-Square test. Time to event analysis (overall [OS] and leukemia-free survival [LFS]) was performed using the method of Kaplan–Meier, with death (for OS), AML progression (for LFS), and allogeneic hematopoietic stem cell transplantation (HSCT; for OS and LFS) used as censors. Statistical analyses were performed with SPSS software, version 28 (IBM-Corp), JMP Pro 15.1.0 software from SAS Institute (Cary, NC) and Statistical Package R version 4.1.1. In the present study we retrospectively analyzed 520 patients with MF, 325 (62.5%) PMF including 156 (48%) pre-fibrotic and 169 (52%) overt PMF, and 195 (37.5%) SMF, including 85 (44%) PPV-MF and 110 (56%) PET-MF. Considering PMF patients, median age was 59 years and 62% were males; median follow-up was 4.5 years (range, 0.06–36) (Table S1). Driver mutations distribution was 58% JAK2, 26% CALR, 6% MPL, 12% triple negative (TN) and 2% double driver-mutated. The numbers of documented deaths and leukemic transformations were 121 (37%) and 37 (12%), respectively. The most frequent non-driver mutated genes were ASXL1 (30%), TET2 (20%) and SRSF2 (10%). SF3B1 mutations were detected in 22 patients (7%; 14 overt-PMF and 8 pre-fibrotic MF) as follows: 11 (50%) K666T/N/M, 7 (32%) K700E, 2 (9%) H662Q/Y, 1 (4%) R625L and 1 (4%) N626S. Of the 19/22 SF3B1 mutated patients, 12 (54%) were JAK2V617F, 6 (27%) CALR and 1 (4%) MPL mutated. Considering SF3B1 mutated patients, the most frequent non-driver co-mutated genes included ASXL1 (n = 4; 18%) and TET2 (n = 3; 14%) followed by DNMT3A, EZH2 and TP53 (n = 2; 9% for each). SF3B1 mutated PMF patients were mostly male (86% vs. 60%; p = .01), older (65 vs. 62 years; p = .004) and displayed a quite significant higher platelet count (561 × 109/L vs. 402 × 109/L; p = .06), and lower Hb levels (11.3 g/dL vs. 12.3 g/dL; p = .09) than the wild-type counterpart. Among a total of 195 SMF patients included in the analysis, median age was 63 years and 55% were male; median follow-up was 3.9 years (range, 0.07–28). There were 80 (41%) documented deaths and 27 (14%) leukemic transformations (Table S2). Driver mutations were distributed as follows: 70% JAK2V617F, 23% CALR, 8% MPL, and 2% TN; 13 (7%) patients were double driver-mutated. The most frequent non-driver mutated genes were ASXL1 (30%), TET2 (18%) and ZRSR2 (8%). SF3B1 mutation was detected in 10 patients (5%) and included 4 (40%) K666T/N/M, 4 (40%) K700E, 1 (10%) G751V, 1 (10%) G742D. All SF3B1 mutated patients carried also a driver mutation; in particular, 5 (50%) were JAK2V617F, 4 (40%) CALR and 1 (10%) MPL mutated. Considering SF3B1 mutated patients, the pattern of most frequent non-driver co-mutated genes included TET2 (n = 4; 40%) followed by CBL, RUNX1 and TP53 (n = 2; 20% for each). SF3B1 mutated SMF patients were older (66 vs. 62 years; p = .09) and displayed splenomegaly less frequently (p = .05). Moreover, correlative analysis revealed that SF3B1 mutation was mostly associated with CBL (p = .004), RUNX1 (p = .02) and KRAS (p = .03) mutations, in comparison with SF3B1 wild-type counterpart. We then investigated the impact of SF3B1 mutation on OS and LFS. Considering PMF patients, SF3B1 mutation did not impact on OS (HR 1.1; 95% CI 0.6–2; p = .8, Figure 1A) and LFS (HR 0.7; 95% CI 0.2–2.8; p = .6, not shown). Conversely, considering SMF cases, OS was negatively affected by SF3B1 mutation compared to wild-type counterpart (HR 3.2; 95% CI 1.5–7; p = .002, Figure 1B) whereas LFS resulted similar (HR 1.1; 95% CI 0.1–8.4; p = .9; not shown). Of note, in two cases of post-ET SMF an SF3B1 mutation was already present in the ET phase of disease. With the aim to better decipher the individual contribution of SF3B1 mutation on OS in SMF patients, we performed a Cox regression analysis that included both clinical and molecular variables (Table S3). In addition to SF3B1, univariate analysis identified age ≥ 65 years (HR 5; 95% CI 3.1–8.3; p = .003), leukocytes >25 × 109/L (HR 2, 95% CI 1.1–3.7; 0.03), Hb < 11 g/dL (HR 3.1, 95% CI 2–5.1; p < .0001), platelet count <150 × 109/L (HR 2.7 95% CI 1.6–4.6; p = .0003), circulating blasts ≥3% (HR 4, 95% CI 2.1–7.5; p < .0001) and mutations of CBL (HR 4, 95% CI 1.7–9.3; p = .001), SRSF2 (HR 8.1, 95% CI 2.5–26.7; p = .0006), U2AF1 (HR 2.7, 95% CI 1.2–6; p = .02) and TP53 (HR 2.9, 95% CI 1.4–5.8; p = .003) as risk factors for a reduced overall survival. In the context of all the aforementioned risk factors, multivariable analysis confirmed the independent prognostic contribution of mutated SF3B1 (HR 2.8, 95% CI 1.2–6.3; 0.02), along with older age (p < .0001), hemoglobin <11 g/dL (p = .02), circulating blasts ≥3% (p = .0003) and CBL mutations (p = .01). The low number of cases prevented us by analyzing the individual prognostic contribution of different SF3B1 mutations. SF3B1, along with SRSF2 and U2AF1 are the three most frequently mutated spliceosome genes in the context of myeloid neoplasms; in PMF, SRSF2 and U2AF1 (mainly Q157) mutations, but not SF3B1, were associated with inferior survival and are incorporated in the mutation-enhanced international prognostic systems (MIPSS)-70/v2.0 scores.9, 10 In the present study we analyzed, separately, the clinical, molecular and prognostic correlates of SF3B1 mutations in PMF and SMF. We confirmed the infrequent occurrence of SF3B1 mutations in both PMF (7%) and SMF (5%); 80% of the latter were in the post-ET subset. All the reported spliceosome gene mutations were mutually exclusive. Importantly, in multivariable analysis, also including clinical and molecular variables comprising the MYSEC-PM11 prognostic score, SF3B1 mutations were independently associated with reduced OS in SMF (Table S3). Recently, spliceosome gene mutations occurring in patients with ET (SF3B1, SRSF2 and U2AF1) and PV (SRSF2) were included in MIPSS PV-ET12 prognostic score, since they adversely affected OS. The principal limitation of the current study is its retrospective nature; moreover, despite the high number of MF patients included, the number of SF3B1 mutated PMF and SMF cases remained limited, making the findings reported herein largely explorative, requiring validation in larger independent cohorts. However, along with recent data that highlighted a differential prognostic role of ASXL1 mutations in PMF versus SMF,13 these findings strengthen the concept that PMF and SMF represent two largely different biological entities. In this regard, the development of integrated prognostic models specific for patients with SMF remains an unmet clinical need. Giuseppe G. Loscocco, Paola Guglielmelli, Francesco Passamonti, and Alessandro M. Vannucchi designed research, analyzed and interpreted data. Giuseppe G. Loscocco, Paola Guglielmelli, Francesco Mannelli, Barbara Mora, Carmela Mannarelli, Giada Rotunno, Fabiana Pancani, Chiara Maccari, Niccolò Bartalucci, Simone Romagnoli, Giacomo Coltro contributed analytical tools and data. Giuseppe G. Loscocco and Giacomo Coltro performed statistical analysis; Giuseppe G. Loscocco, Paola Guglielmelli, Francesco Passamonti and Alessandro M. Vannucchi wrote the manuscript. All authors participated in collecting, analyzing, interpreting the data, checked and approved the final version of the manuscript. This work was supported by Associazione Italiana per la Ricerca sul Cancro (AIRC) 5x1000 call "Metastatic disease: the key unmet need in oncology" to MYNERVA (MYeloid NEoplasms Research Venture AIRC), project #21267; Ministero della Università e della Ricerca PRIN-2017WXR7ZT and from the Ministero della Salute, Rome, Italy (Finalizzata 2018, NET-2018-12365935), personalized medicine program on myeloid neoplasms: characterization of the patient's genome for clinical decision making and systematic collection of real world data to improve quality of health care. The authors have no relevant conflict of interest related to this manuscript. The data that support the findings of this study are available from the corresponding author upon reasonable request. Table S1. Clinical and laboratory characteristics of PMF patient according to presence/absence of SF3B1 mutation Table S2. Clinical and laboratory characteristics of SMF patient according to presence/absence of SF3B1 mutation Table S3. Univariate and multivariable analysis for OS in SMF patients. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.